Spatio-Temporal Analysis of Urban Data and its Application for Smart Cities
| dc.contributor.author | Gupta, Prakriti | en |
| dc.contributor.committeechair | Zeng, Haibo | en |
| dc.contributor.committeemember | Abbott, A. Lynn | en |
| dc.contributor.committeemember | Huang, Bert | en |
| dc.contributor.department | Electrical and Computer Engineering | en |
| dc.date.accessioned | 2019-02-03T07:00:50Z | en |
| dc.date.available | 2019-02-03T07:00:50Z | en |
| dc.date.issued | 2017-08-11 | en |
| dc.description.abstract | With the advent of smart sensor devices and Internet of Things (IoT) in the rapid urbanizing cities, data is being generated, collected and analyzed to solve urban problems in the areas of transportation, epidemiology, emergency management, economics, and sustainability etc. The work in this area basically involves analyzing one or more types of data to identify and characterize their impact on other urban phenomena like traffic speed and ride-sharing, spread of diseases, emergency evacuation, share market and electricity demand etc. In this work, we perform spatio-temporal analysis of various urban datasets collected from different urban application areas. We start with presenting a framework for predicting traffic demand around a location of interest and explain how it can be used to analyze other urban activities. We use a similar method to characterize and analyze spatio-temporal criminal activity in an urban city. At the end, we analyze the impact of nearby traffic volume on the electric vehicle charging demand at a charging station. | en |
| dc.description.abstractgeneral | Because of the ubiquity of the Internet and smart devices, a tremendous amount of data has been collected from multiple sources like vehicles, purchasing details, online searches etc., which is being used to develop innovative applications. These applications aim to improve economic, social and personal lives of people through new start-of-the-art techniques like machine learning and data analytics. With this motivation in mind, we present three applications leveraging the data collected from urban cities to improve the life of people living in such cities. First, we start by using taxi trip data, collected around a given location, and use it to develop a model that can predict taxi demand for next half hour. This model can be used to schedule advertisements or dispatch taxis depending upon the demand. Second, using a similar mathematical approach, we propose a strategy to predict the number of crimes that can happen at a given location on the next day. This helps in maintaining law and order in the city. As our third and last application, we use the traffic and historical charging data to predict electric vehicle charging demand for the next day. Electricity generating power plants can use this model to prepare themselves for the higher demand emerged because of the increasing use of electric vehicles. | en |
| dc.description.degree | Master of Science | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:12351 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/87418 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Traffic Analysis | en |
| dc.subject | Electric Vehicle Charging Demand | en |
| dc.subject | Quantitative Criminology | en |
| dc.subject | Spatio-Temporal Analysis | en |
| dc.subject | Data Mining | en |
| dc.title | Spatio-Temporal Analysis of Urban Data and its Application for Smart Cities | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Computer Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | Master of Science | en |